Existing de-biasing baselines consider binary attributes [1]}.
For a fair comparison with them, we adopted their setting to use binary classifiers trained on CelebA [2]} for de-biasing and evaluation. Since classifiers trained on CelebA also suffer from the spurious correlation between attributes, we did not use the moment constraint for this experiment. Instead, since PromptGen allows conditional image generation with the classifier energy, we generated the same number of samples conditioned on each attribute or attribute combination.
Table REF  shows that PromptGen has competitive performance on de-biasing for attributes and attribute combinations.
